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A knowledge-based perspective for software process modeling

Noureddine Kerzazi, Mathieu Lavallée and Pierre N. Robillard

Article (2013)

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Cite this document: Kerzazi, N., Lavallée, M. & Robillard, P. N. (2013). A knowledge-based perspective for software process modeling. e-Informatica Software Engineering Journal, 7(1), p. 25-33. doi:10.5277/e-inf130103
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As the acquisition and sharing of knowledge form the backbone of the software development process, it is important to identify knowledge discrepancies between the process elements. Explicit representation of the knowledge components within a software process model can provide a means to expose these discrepancies. This paper presents an extension of the Software and System Process Engineering Metamodel (SPEM), to be used as a new knowledge modeling layer. The approach, which is based on ontologies for knowledge representation, constitutes an explicit method for representing knowledge within process models. A concept matching indicator shows the state of the process model in terms of the concept gaps for each task within the process. This indicator could lead to more informed decision making and better management of the associated risks, in terms of team competency, documentation quality, and the training required to mitigate them.

Open Access document in PolyPublie
Subjects: 2700 Technologie de l'information > 2700 Technologie de l'information
2700 Technologie de l'information > 2717 Études de modélisation et de simulation
Department: Département de génie informatique et génie logiciel
Funders: Fonds de recherche sur la nature et les technologies (FQRNT), CRSNG / NSERC
Grant number: 127037, A0141, 361163
Date Deposited: 20 Feb 2019 13:13
Last Modified: 08 Apr 2021 10:43
PolyPublie URL: https://publications.polymtl.ca/3636/
Document issued by the official publisher
Journal Title: e-Informatica Software Engineering Journal (vol. 7, no. 1)
Publisher: Polish Academy of Sciences and Wrocław University of Science and Technology - Faculty of Computer Science and Management
Official URL: https://doi.org/10.5277/e-inf130103


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